← Back to Event List

DE Seminar: Christopher Kim



Mathematics/Psychology : 401

Date & Time

April 1, 2024, 11:00 am12:00 pm


TitleLearning to generate cortical activity in strongly recurrent spiking neural networks

Speaker: Christopher Kim

AbstractSpiking neural networks with balanced excitation and inhibition are widely used for capturing canonical features of cortical activity, such as spiking variability. However, due to their large network size and strong recurrent dynamics, it is challenging to train these networks to perform complex tasks, hindering our understanding of their computational properties. In this talk, we present a recursive least-squares method that can train spiking neural networks to generate arbitrarily complex activity patterns. We show that when a subset of neurons embedded in a balanced excitatory-inhibitory network is trained to produce task-related neural activities recorded from the motor cortex, the learned activity spreads to the rest of the network, promoting distributed representation of task variables in cortical networks. We demonstrate GPU-implementation of the training method, which enables fast training of large scale networks. In sum, our work opens the opportunity to develop and investigate strongly recurrent spiking neural networks driven by experimentally recorded neural data.